CHRIS

Chris Traffanstedt 

1 posts

Re:Module 7 DQ 2

The initial perception of this information is that we have a negative correlation, which can be seen in the two variables going in the opposite direction (Gravetter & Wallnau, 2010). This perception can be seen in our example by the number -.21. The next perception is that there is no significance in the correlation (for .21 is not less than the p of .0001). Thus we would conclude that there is not enough evidence to suggest that drug X is connected to smaller tumor size in patients.

 

Another way to explore the correlation in our example would be to use the Spearman correlation. Specifically, to use the Spearman correlation to measure the consistency of the relationship (Gravetter & Wallnau, 2010) between drug X and the size of tumors. The goal here is to put the measurements in ranks and then see if the variables are consistently related and thus see their rank linearly related (Gravetter & Wallnau, 2010). The null hypothesis here would be to see that the population correlation is zero and the alternative hypothesis is no zero.

 

Overall, the goal here is to show significant correlation not simply show that there is a connection. We must remember that correlation is not the same as cause. The researcher must be diligent to show the data can be plotted linearly and therefore a strong correlation.

 

 

Gravetter, F. J., & Wallnau, L. B. (2010). Statistics for the behavioral sciences (9th ed.). Belmont, CA: Wadsworth Cengage Learning.